Many teams are using AI as a faster way of executing existing processes. The value of AI is realized when it is utilized in a process to design, test, and improve how work should naturally flow before it is automated.
Rather than speeding up an existing process that is flawed in its design, AI can help a team find ways of doing things better.
Use AI to Understand How Work Actually Happens
When designing a process, a whiteboard is often empty, and a team begins with an idealistic vision of how work should happen.
With AI, a team can begin with how work actually happens instead of how it should happen.
By analyzing existing data such as support requests, email conversations, task histories, standard operating procedures, and meeting notes, AI can help a team identify common patterns in how work is actually flowing. AI can also help a team identify common bottlenecks that happen in a process and common scenarios that happen in a process.
By doing this, a team can create a realistic vision of how a process is actually flowing and identify ways to improve it.
Generate Process Design Options
AI may also be used as a “drafting partner” to help design new workflow.
After establishing the objectives and constraints of a workflow, such as industry needs, team size, systems to be included, and risks, teams may ask AI to suggest various ways of organizing a workflow.
These suggestions might include:
- Different sequences of tasks
- Alternative approval structures
- Variations in responsibility between roles
Although not optimal, there are several options to choose from, which teams can use, modify, and integrate. This is often much less labor-intensive than developing a process design from scratch.
Stress Test the Process Before Implementation
Prior to the rollout of a new process, AI can be utilized to test it against possible scenarios.
For instance, teams can present possible scenarios and ask questions such as:
- What happens if a customer does not respond at a critical step?
- Where does a failed task appear in the workflow?
- How does the process handle missing information or unexpected delays?
Possible failure points, unnecessary loops, and handoffs can be identified, and suggestions for simplification can be provided by highlighting steps that are possibly redundant or can be combined safely.
This ensures that the process does not become overly complicated and still appears logical on the design chart.
Turn Process Design into Practical Tools
After the process design has been decided on, AI can help turn it into useful resources for the teams.
From a single process outline, AI can generate:
- Draft standard operating procedures
- Task checklists
- Forms and field definitions
- Role descriptions
- Training guides or scripts
This enables teams to progress rapidly from design to implementation without leaving critical documentation incomplete.
Support Continuous Process Improvement
AI is also valuable after the process has been implemented.
This is because, by analyzing operational data such as task history, comments, and exceptions, AI is able to recognize patterns that demonstrate areas where the process is not functioning correctly. For example, it may recognize that certain steps are frequently skipped, that approvals do not influence outcomes, or that certain cases are responsible for most of the rework.
This is a clear starting point for future improvements.
AI as a Process Design Partner
In this way, AI is not simply a tool for executing a workflow:
It is a partner in designing a process, assisting in the discovery of how a process really works, and exploring and testing options to improve it.